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dc.contributor.authorZhang, Huigang
dc.contributor.authorBai, Xiao
dc.contributor.authorZheng, Huaxin
dc.contributor.authorZhao, Huijie
dc.contributor.authorZhou, Jun
dc.contributor.authorCheng, Jian
dc.contributor.authorLu, Hanqing
dc.date.accessioned2018-03-12T12:30:34Z
dc.date.available2018-03-12T12:30:34Z
dc.date.issued2013
dc.date.modified2013-06-28T02:04:37Z
dc.identifier.issn1545-598X
dc.identifier.doi10.1109/LGRS.2012.2207087
dc.identifier.urihttp://hdl.handle.net/10072/52050
dc.description.abstractSegmentation and classification are important tasks in remote sensing image analysis. Recent research shows that images can be described in hierarchical structure or regions. Such hierarchies can produce the state-of-the-art segmentations and can be used in the classification. However, they often contain more levels and regions than required for an efficient image description, which may cause increased computational complexity. In this letter, we propose a new hierarchical segmentation method that applies graph Laplacian energy as a generic measure for segmentation. It reduces the redundancy in the hierarchy by an order of magnitude with little or no loss of performance. In the classification stage, we apply local self-similarity feature to capture the internal geometric layouts of regions in an image. By incorporating advantages from both semantic hierarchical segmentation and local geometric region description, we have achieved better performance than those from the methods being compared. In the experimental section, we validate the effectiveness of our method by showing results on QuickBird and GeoEye-1 image data sets.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent410629 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherIEEE
dc.publisher.placeUnited States
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom396
dc.relation.ispartofpageto400
dc.relation.ispartofissue2
dc.relation.ispartofjournalIEEE Geoscience and Remote Sensing Letters
dc.relation.ispartofvolume10
dc.rights.retentionY
dc.subject.fieldofresearchImage Processing
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchElectrical and Electronic Engineering
dc.subject.fieldofresearchGeomatic Engineering
dc.subject.fieldofresearchcode080106
dc.subject.fieldofresearchcode0801
dc.subject.fieldofresearchcode0906
dc.subject.fieldofresearchcode0909
dc.titleHierarchical remote sensing image analysis via graph Laplacian energy
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.rights.copyright© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.date.issued2013
gro.hasfulltextFull Text
gro.griffith.authorZhou, Jun


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